TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a family of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can remove up to 96% of training tokens without harming quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this mixture outperform much larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.
By Milan Gritta, Patrik Lambert, Jihye Back, Amril Nazir
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli
arXiv:2610.18818v2 Announce Type: cross
Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...
By Divyanshu Bathla, Parag Singla, Jitin Singla
The paper introduces ReMova, a pipeline for cleaning Belarusian data and fine‑tuning large language models (LLMs) for English‑to‑Belarusian translation. It uses a correction tool to handle the two orthographies of Belarusian, remove noise, filter out interference from other languages, and correct common misspellings found online. Ablation experiments on unfiltered data show that filtering benefits all fine‑tuned models, with LLM‑based models gaining about twice as much as a dedicated encoder‑decoder MT system, highlighting data quality as a key bottleneck for Belarusian MT.
By Mikita Pilinka, Aliaksandr Kliuje\u{u}, David Samuel, Yves Scherrer
The study evaluates Arabic–Russian machine translation by comparing seven fine‑tuned neural machine translation (NMT) models with four few‑shot large language models (LLMs) on a new 15.47 million‑pair corpus split into 20k/5k/5k. Fine‑tuned NLLB‑1.3B achieves the best performance (BLEU 16.3, COMET 0.738), while the best few‑shot LLM, Aya‑Expanse 8B, scores only BLEU 1.7 on 500 sentences. Error analysis shows that low lexical overlap between Arabic and Russian is the main source of failures, and statistical tests confirm significant performance gaps between most models.
By Mullosharaf K. Arabov
The paper proposes a multilingual adaptation of an English quality classifier to select high‑quality training data for large language model pretraining across more than 100 languages. The method trains a small multi‑layer perceptron on Transformer embeddings, using machine‑translated text scored by the English classifier as labels. Experiments on 1B, 3B, and 8B parameter models show that the adapted classifier preserves benchmark performance and regional knowledge, and it generalizes to unseen languages by learning the original scoring criteria.
By Vinko Sabol\v{c}ec, Bettina Messmer, Yassine Turki, Martin Jaggi